C++ PYTHON NODE

{ "github_url":"https://github.com/sophgo/tdl_models/tree/main/", "_comment":"model_list specify the model maintained in tdl_models,each model should at least have file name and rgb_order(choice:rgb,bgr,gray)", "model_list":{ "MBV2_DET_PERSON":{ "types":["person"], "file_name":"mbv2_det_person_256_448_INT8", "rgb_order":"rgb" }, "YOLOV8N_DET_HAND":{ "types":["hand"], "file_name":"yolov8n_det_hand_384_640_INT8" }, "YOLOV8N_DET_PET_PERSON":{ "types":["cat","dog","person"], "file_name":"yolov8n_det_pet_person_384_640_INT8" }, "YOLOV8N_DET_BICYCLE_MOTOR_EBICYCLE":{ "types":["bicycle","motorcycle","ebicycle"], "file_name":"yolov8n_det_bicycle_motor_ebicycle_384_640_INT8" }, "YOLOV8N_DET_PERSON_VEHICLE":{ "types":["car","bus","truck","rider with motorcycle","person","bike","motorcycle"], "file_name":"yolov8n_det_person_vehicle_384_640_INT8" }, "YOLOV8N_DET_HAND_FACE_PERSON":{ "types":["hand","face","person"], "file_name":"yolov8n_det_hand_face_person_384_640_INT8" }, "YOLOV8N_DET_FACE_HEAD_PERSON_PET":{ "types":["face","head","person","pet"], "file_name":"yolov8n_det_face_head_person_pet_384_640_INT8" }, "YOLOV8N_DET_HEAD_PERSON":{ "types":["head","person"], "file_name":"yolov8n_det_head_person_384_640_INT8" }, "YOLOV8N_DET_HEAD_HARDHAT":{ "types":["head","hardhat"], "file_name":"yolov8n_det_head_hardhat_576_960_INT8" }, "YOLOV8N_DET_FIRE_SMOKE":{ "types":["fire","smoke"], "file_name":"yolov8n_det_fire_smoke_384_640_INT8" }, "YOLOV8N_DET_FIRE":{ "types":["fire"], "file_name":"yolov8n_det_fire_384_640_INT8" }, "YOLOV8N_DET_HEAD_SHOULDER":{ "types":["head shoulder"], "file_name":"yolov8n_det_head_shoulder_384_640_INT8" }, "YOLOV8N_DET_LICENSE_PLATE":{ "types":["license plate"], "file_name":"yolov8n_det_license_plate_384_640_INT8" }, "YOLOV8N_DET_TRAFFIC_LIGHT":{ "types":["red","yellow","green","off","wait on"], "file_name":"yolov8n_det_traffic_light_384_640_INT8" }, "YOLOV8N_DET_MONITOR_PERSON":{ "types":["person"], "file_name":"yolov8n_det_monitor_person_256_448_INT8" }, "YOLOV11N_DET_MONITOR_PERSON":{ "types":["person"], "file_name":"yolov11n_det_monitor_person_384_640_INT8" }, "YOLOV11N_DET_BICYCLE_MOTOR_EBICYCLE":{ "types":["bicycle","motorcycle","ebicycle"], "file_name":"yolov11n_det_bicycle_motor_ebicycle_384_640_INT8" }, "YOLOV5_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov5s_det_coco80_640_640_INT8" }, "YOLOV6_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov6n_det_coco80_640_640_INT8" }, "YOLOV7_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov7_tiny_det_coco80_640_640_INT8" }, "YOLOV8_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov8n_det_coco80_640_640_INT8" }, "YOLOV10_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov10n_det_coco80_640_640_INT8" }, "YOLOV11N_DET_COCO80":{ "is_coco_types":true, "file_name":"yolov11n_det_coco80_640_640_INT8" }, "PPYOLOE_DET_COCO80":{ "is_coco_types":true, "file_name":"ppyoloe_det_coco80_640_640_INT8" }, "YOLOX_DET_COCO80":{ "is_coco_types":true, "file_name":"yolox_m_det_coco80_640_640_INT8" }, "YOLOV5":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"" }, "YOLOV6":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"" }, "YOLOV8":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"best", "model_path": "/root/best.cvimodel", "model_type": "yolov8", "input_width": 640, "input_height": 640, "num_classes": 3, "threshold": 0.5, "nms_threshold": 0.45, "mean": [0.0, 0.0, 0.0], "scale": [0.00392157, 0.00392157, 0.00392157], "format": "RGB", "labels": ["Ades", "Barbie", "Bauer"] }, "YOLOV10":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"" }, "PPYOLOE":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"" }, "YOLOX":{ "_comment":"custom model, specify num_cls or branch string", "file_name":"" }, "SCRFD_DET_FACE":{ "_comment":"output face and 5 landmarks", "types":["face"], "file_name":"scrfd_det_face_432_768_INT8" }, "CLS_ATTRIBUTE_GENDER_AGE_GLASS":{ "_comment":"output age,gender(0:male,1:female),glass(0:no glass,1:glass)", "types":["age","gender","glass"], "file_name":"cls_attribute_gender_age_glass_112_112_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_ATTRIBUTE_GENDER_AGE_GLASS_MASK":{ "_comment":"output age,gender(0:male,1:female),glass(0:no glass,1:glass),mask(0:no mask,1:mask)", "types":["age","gender","glass","mask"], "file_name":"cls_attribute_gender_age_glass_mask_112_112_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_ATTRIBUTE_GENDER_AGE_GLASS_EMOTION":{ "_comment":"output age,gender(0:male,1:female),glass(0:no glass,1:glass),emotion(0:anger,1:disgut,2:fear,3:happy,4:neutral,5:sad;6:surprise)", "types":["age","gender","glass","emotion"], "file_name":"cls_attribute_gender_age_glass_emotion_112_112_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_RGBLIVENESS":{ "_comment":"output 0:live or 1:spoof", "types":["live","spoof"], "file_name":"cls_rgbliveness_256_256_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_YOLOV8":{ "file_name":"yolov8_cls_384_640_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_HAND_GESTURE":{ "_comment":"output hand gesture(0:fist,1:five,2:none,3:two)", "types":["fist","five","none","two"], "file_name":"cls_hand_gesture_128_128_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[255.0,255.0,255.0] }, "CLS_KEYPOINT_HAND_GESTURE":{ "_comment":"output hand gesture(0:fist,1:five,2:four,3:none,4:ok,5:one,6:three,7:three2,8:two)", "types":["fist","five","four","none","ok","one","three","three2","two"], "file_name":"cls_keypoint_hand_gesture_1_42_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[1.0,1.0,1.0] }, "CLS_SOUND_BABAY_CRY":{ "_comment":"output 0:background or 1:cry,single channel", "types":["background","cry"], "file_name":"cls_sound_babay_cry_188_40_INT8", "rgb_order":"gray" }, "CLS_SOUND_COMMAND_NIHAOSHIYUN":{ "_comment":"single channel,TODO:add types", "types":["background","nihaoshiyun"], "file_name":"cls_sound_nihaoshiyun_126_40_INT8", "rgb_order":"gray", "hop_len":128, "fix":1 }, "CLS_SOUND_COMMAND_NIHAOSUANNENG":{ "_comment":"single channel,TODO:add types", "types":["background","nihaosuanneng"], "file_name":"my_custom_sound_command", "rgb_order":"gray", "hop_len":128, "fix":1 }, "CLS_SOUND_COMMAND_XIAOAIXIAOAI":{ "_comment":"single channel,TODO:add types", "types":["background","xiaoaixiaoai"], "file_name":"cls_sound_xiaoaixiaoai_126_40_INT8", "rgb_order":"gray", "hop_len":128, "fix":1 }, "CLS_IMG":{ "_comment":"custom classification, set types,file_name,specify rgb order and mean/std", "types":["custom"], "file_name":"", "rgb_order":"rgb" }, "KEYPOINT_LICENSE_PLATE":{ "_comment":"output 4 license plate keypoints", "types":["top_left","top_right","bottom_left","bottom_right"], "file_name":"keypoint_license_plate_64_128_INT8", "rgb_order":"rgb" }, "KEYPOINT_HAND":{ "_comment":"output 21 hand keypoints", "file_name":"keypoint_hand_128_128_INT8", "rgb_order":"rgb" }, "KEYPOINT_YOLOV8POSE_PERSON17":{ "_comment":"output 17 person keypoints and box", "file_name":"keypoint_yolov8pose_person17_384_640_INT8", "rgb_order":"rgb" }, "KEYPOINT_SIMCC_PERSON17":{ "_comment":"output 17 person keypoints from cropped image", "file_name":"keypoint_simcc_person17_256_192_INT8", "rgb_order":"rgb" }, "KEYPOINT_FACE_V2": { "_comment": "KEYPOINT_FACE_V2", "file_name": "keypoint_face_v2_64_64_INT8" }, "LSTR_DET_LANE":{ "_comment":"output lane keypoints", "file_name":"lstr_det_lane_360_640_MIX", "rgb_order":"rgb" }, "RECOGNITION_LICENSE_PLATE":{ "_comment":"output 7 license plate characters", "file_name":"recognition_license_plate_24_96_MIX", "rgb_order":"bgr" }, "YOLOV8_SEG":{ "_comment":"custom segmentation,set types,file_name,specify rgb order", "types":[], "file_name":"yolov8_seg_384_640_INT8" }, "YOLOV8_SEG_COCO80":{ "is_coco_types":true, "_comment":"output 80 segmentation mask", "file_name":"yolov8n_seg_coco80_640_640_INT8" }, "TOPFORMER_SEG_PERSON_FACE_VEHICLE":{ "_comment":"output mask", "types":["background","person","face","vehicle","license plate"], "file_name":"topformer_seg_person_face_vehicle_384_640_INT8", "rgb_order":"rgb" }, "FEATURE_IMG":{ "_comment":"custom segmentation,set file_name,specify rgb order,set mean/std", "file_name":"", "rgb_order":"rgb" }, "FEATURE_CLIP_IMG":{ "_comment":"clip image feature extraction", "file_name":"feature_clip_image_224_224_W4BF16", "rgb_order":"rgb" }, "FEATURE_CLIP_TEXT":{ "_comment":"clip text feature extraction", "file_name":"feature_clip_text_1_77_W4BF16", "rgb_order":"rgb" }, "FEATURE_MOBILECLIP2_IMG":{ "_comment":"mobileclip2 image feature extraction", "file_name":"feature_mobileclip2_B_img_224_224_INT8", "rgb_order":"rgb" }, "FEATURE_MOBILECLIP2_TEXT":{ "_comment":"mobileclip2 text feature extraction", "file_name":"feature_mobileclip2_B_text_1_77_INT8", "rgb_order":"rgb" }, "FEATURE_CVIFACE":{ "_comment":"cviface 256-dimensional feature", "file_name":"feature_cviface_112_112_INT8", "rgb_order":"rgb", "mean":[127.5,127.5,127.5], "std":[128,128,128] }, "FEATURE_BMFACE_R34":{ "_comment":"output 512 dim feature", "file_name":"feature_bmface_r34_112_112_INT8", "rgb_order":"rgb", "mean":[0,0,0], "std":[1,1,1] }, "FEATURE_BMFACE_R50":{ "_comment":"output 512 dim feature", "file_name":"bmface_r50_v1_bmnetp.bmodel", "rgb_order":"rgb", "mean":[0,0,0], "std":[1,1,1] }, "TRACKING_FEARTRACK":{ "_comment":"single object tracking", "file_name":"tracking_feartrack_128_128_256_256_INT8", "rgb_order":"rgb", "mean":[123.675,116.28,103.53], "std":[58.395,57.12,57.375] }, "RECOGNITION_SPEECH_ZIPFORMER_ENCODER":{ "file_name":"recognition_speech_zipformer_encoder-s_71_80_BF16" }, "RECOGNITION_SPEECH_ZIPFORMER_DECODER":{ "file_name":"recognition_speech_zipformer_decoder-s_1_2_BF16" }, "RECOGNITION_SPEECH_ZIPFORMER_JOINER":{ "file_name":"recognition_speech_zipformer_joiner-s_1_512_1_512_BF16" } } } ================= !!!!!!!!!!!!!!!!!!!!!!!! solucao tdl_sdk !!!!!!!!!!!!!!!!!!!!!!! em https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-obb.ipynb#scrollTo=tdSMcABDNKW- !pip install ultralytics==8.2.103 -q faça o upgrade treine from ultralytics import YOLO model = YOLO('yolov8n.pt') results = model.train(data=f"data.yaml", epochs=100, imgsz=640) https://github.com/milkv-duo/duo-buildroot-sdk-v2/blob/develop/tdl_sdk/tool/yolo_export/yolov8_export.py model_transform.py \ --model_name yolov8n \ --model_def best.onnx \ --input_shapes [[1,3,640,640]] \ --mean 0.0,0.0,0.0 \ --scale 0.0039216,0.0039216,0.0039216 \ --keep_aspect_ratio \ --pixel_format rgb \ --mlir yolov8n.mlir fotos na pasta BUGGIO estavam 640x640 run_calibration.py yolov8n.mlir \ --dataset ../../BUGGIO \ --input_num 100 \ -o yolov8n_cali_table model_deploy.py \ --mlir yolov8n.mlir \ --quant_input --quant_output \ --quantize INT8 \ --calibration_table yolov8n_cali_table \ --processor cv181x \ --model yolov8n_cv181x_int8_sym.cvimodel python tdl sdk sophgo example tdl_sophgo.py python tdl_sophgo.py /root/cv181x/yolov8n_cv181x_int8_sym_cv181x.cvimodel Barbie_5-4_jpg.rf.64feb144416c82dc7c58b335d7143774.jpg import sys import os from tdl import nn, image import cv2 import numpy as np if __name__ == "__main__": if len(sys.argv) != 3: print("Usage: python sample_fd.py <model_path> <image_path>") sys.exit(1) model_path = sys.argv[1] img_path = sys.argv[2] face_detector = nn.get_model(nn.ModelType.YOLOV8, model_path) img = image.read(img_path) # img = cv2.imread(img_path) bboxes = face_detector.inference(img) print(bboxes) #https://github.com/sophgo/tdl_sdk/tree/master python tdl sdk milkv-duo S example sample_img_object_detection.py python sample_img_object_detection.py YOLOV8 /root/cv181x/yolov8n_cv181x_int8_sym_cv181x.cvimodel /root/Bauer_9-4_jpg.rf.1ee8c79f82e5c4ed6b2ba3b7d5340d2c.jpg import sys import os from tdl import nn, image import cv2 import numpy as np def visualize_objects(img_path, bboxes, save_path="object_detection.jpg"): """可视化目标检测结果""" img = cv2.imread(img_path) print(f"检测到 {len(bboxes)} 个目标") for i, obj in enumerate(bboxes): x1, y1, x2, y2 = map(int, [obj['x1'], obj['y1'], obj['x2'], obj['y2']]) class_id = obj['class_id'] score = obj['score'] class_name = obj.get('class_name', f'class_{class_id}') cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2) label = f"{class_id}:{score:.2f}" center_x = (x1 + x2) // 2 center_y = (y1 + y2) // 2 cv2.putText(img, label, (center_x, center_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) cv2.imwrite(save_path, img) print(f"保存图像到: {save_path}") if __name__ == "__main__": if len(sys.argv) < 4 or len(sys.argv) > 5: print("Usage: python3 sample_img_object_detection.py <model_id_name> <model_dir> <image_path> [threshold]") # 尚未加入对于不是检测模型的处理 sys.exit(1) model_id_name = sys.argv[1] model_dir = sys.argv[2] image_path = sys.argv[3] threshold = float(sys.argv[4]) if len(sys.argv) == 5 else 0.5 if not os.path.exists(image_path): print(f"图像文件不存在: {image_path}") sys.exit(1) model_type = getattr(nn.ModelType, model_id_name) model = nn.get_model(model_type, model_dir, device_id=0) # 读取图像 img = image.read(image_path) # 执行推理 outdatas = model.inference(img) expected_keys = {"class_id", "class_name", "score", "x1", "y1", "x2", "y2"} is_detection = ( isinstance(outdatas, list) and isinstance(outdatas[0], dict) and set(outdatas[0].keys()) == expected_keys ) if not is_detection: print("当前模型不是目标检测模型，输出内容：") print(outdatas) sys.exit(1) print(f"out_datas.size: {len(outdatas)}") for i, obj in enumerate(outdatas): print(f"obj_meta_index: {i} " f"class: {obj['class_id']} " f"score: {obj['score']:.2f} " f"bbox: {obj['x1']:.2f} {obj['y1']:.2f} {obj['x2']:.2f} {obj['y2']:.2f}") visualize_objects(image_path, outdatas) # input: python3 sample_img_object_detection.py <model_id_name> <model_dir> <image_path> [threshold] # output: obj_meta_index: <index> class: <class_id> score: <score_value> bbox: <x1> <y1> <x2> <y2> utilizando Script completo ./pt_to_cvimodel_tdl_sdk.sh python yolov8_export.py python export_tdl_sdk.py --dataset ../../BUGGIO --test_input ../../BUGGIO/Ades_2-3_jpg.rf.de3d17a6dcc748c6642882198a1c1c76.jpg best.onnx !!!!!!!!!!!!!!!!!!!!!!!! solucao recamera !!!!!!!!!!!!!!!!!!!!!!! em https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb#scrollTo=tdSMcABDNKW-yolo11s !pip install ultralytics==8.2.103 -q faça o upgrade pip install --upgrade --force-reinstall ultralytics copie o zip do BUGGIO !unzip /context/buggio.v1i.yolov11.zip treine !yolo task=detect mode=train model=yolo11n.pt data=data.yaml epochs=200 imgsz=640 ./pt_to_cvimodel_recamera.sh yolo export model=best.pt format=onnx imgsz=640,640 python export_recamera.py --output_names "/model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0" --dataset ../../BUGGIO --test_input ../../BUGGIO/Barbie_7-10_jpg.rf.502fbff248ff3b2336a9e60317de843b.jpg best.onnx --quantize INT8 testar cvimodel no recamera model_transform --model_name best --model_def ./best.onnx --input_shapes '[[1,3,640,640]]' --mean 0.0,0.0,0.0 --scale 0.0039216,0.0039216,0.0039216 --keep_aspect_ratio --pixel_format rgb --output_names /model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0 --test_input ../../BUGGIO/Ades_2-4_jpg.rf.4de8403c125c5d16b435a839a3a93780.jpg --test_result best_top_outputs.npz --mlir best.mlir run_calibration best.mlir --dataset ../../BUGGIO --input_num 200 -o best_calib_table model_deploy --mlir best.mlir --quantize INT8 --quant_input --processor cv181x --calibration_table best_calib_table --test_input ../../BUGGIO/Ades_2-4_jpg.rf.4de8403c125c5d16b435a839a3a93780.jpg --test_reference best_top_outputs.npz --customization_format RGB_PACKED --fuse_preprocess --aligned_input --model best_cv181x_int8.cvimodel model_transform \ --model_name yolo11n \ --model_def best.onnx \ --input_shapes "[[1,3,640,640]]" \ --mean "0.0,0.0,0.0" \ --scale "0.0039216,0.0039216,0.0039216" \ --keep_aspect_ratio \ --pixel_format rgb \ --output_names "/model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0" \ --test_input ../../BUGGIO/Ades_2-3_jpg.rf.de3d17a6dcc748c6642882198a1c1c76.jpg \ --test_result yolo11n_top_outputs.npz \ --mlir yolo11n.mlir run_calibration \ yolo11n.mlir \ --dataset ../../BUGGIO \ --input_num 100 \ -o yolo11n_calib_table model_deploy \ --mlir yolo11n.mlir \ --quantize INT8 \ --quant_input \ --processor cv181x \ --calibration_table yolo11n_calib_table \ --test_input ../../BUGGIO/Ades_2-3_jpg.rf.de3d17a6dcc748c6642882198a1c1c76.jpg \ --test_reference yolo11n_top_outputs.npz \ --customization_format RGB_PACKED \ --fuse_preprocess \ --aligned_input \ --model yolo11n_1684x_int8_sym.cvimodel =======car counting https://github.com/Seeed-Studio/sscma-example-sg200x/tree/main/solutions/sscma-model/main yolo export model=best.pt format=onnx opset=14 python export_recamera.py --output_names "/model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0" --dataset ../../CARS --test_input ../../CARS/DOH_3-video-converter_com-_mp4-26_jpg.rf.a6a631199f4152b1ab619e3e3cf6e8ee.jpg best.onnx --quantize INT8 model_transform \ --model_name yolo11n \ --model_def best.onnx \ --input_shapes "[[1,3,640,640]]" \ --mean "0.0,0.0,0.0" \ --scale "0.0039216,0.0039216,0.0039216" \ --keep_aspect_ratio \ --pixel_format rgb \ --output_names "/model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0" \ --test_input ../../CARS/DOH_3-video-converter_com-_mp4-26_jpg.rf.a6a631199f4152b1ab619e3e3cf6e8ee.jpg \ --test_result yolo11n_top_outputs.npz \ --mlir yolo11n.mlir run_calibration \ yolo11n.mlir \ --dataset ../../CARS \ --input_num 100 \ -o yolo11n_calib_table model_deploy \ --mlir yolo11n.mlir \ --quantize INT8 \ --quant_input \ --processor cv181x \ --calibration_table yolo11n_calib_table \ --test_input ../../CARS/DOH_3-video-converter_com-_mp4-26_jpg.rf.a6a631199f4152b1ab619e3e3cf6e8ee.jpg \ --test_reference yolo11n_top_outputs.npz \ --customization_format RGB_PACKED \ --fuse_preprocess \ --aligned_input \ --model yolo11n_1684x_int8_sym.cvimodel yolo26n model_transform \ --model_name yolo26n \ --model_def best_26.onnx \ --input_shapes "[[1,3,640,640]]" \ --mean "0.0,0.0,0.0" \ --scale "0.0039216,0.0039216,0.0039216" \ --keep_aspect_ratio \ --pixel_format rgb \ --output_names "/model.23/cv2.0/cv2.0.2/Conv_output_0,/model.23/cv3.0/cv3.0.2/Conv_output_0,/model.23/cv2.1/cv2.1.2/Conv_output_0,/model.23/cv3.1/cv3.1.2/Conv_output_0,/model.23/cv2.2/cv2.2.2/Conv_output_0,/model.23/cv3.2/cv3.2.2/Conv_output_0" \ --test_input ../../BUGGIO/Ades_2-4_jpg.rf.4de8403c125c5d16b435a839a3a93780.jpg \ --test_result yolo26n_top_outputs.npz \ --mlir yolo26n.mlir run_calibration \ yolo26n.mlir \ --dataset ../../BUGGIO \ --input_num 100 \ -o yolo26n_calib_table model_deploy \ --mlir yolo26n.mlir \ --quantize INT8 \ --quant_input \ --processor cv181x \ --calibration_table yolo26n_calib_table \ --test_input ../../BUGGIO/Ades_2-4_jpg.rf.4de8403c125c5d16b435a839a3a93780.jpg \ --test_reference yolo26n_top_outputs.npz \ --customization_format RGB_PACKED \ --fuse_preprocess \ --aligned_input \ --model yolo26n_1684x_int8_sym.cvimodel https://github.com/ultralytics/ultralytics/blob/ee2ac9e43491e5ca61a158fb3a42e621a6710ee1/docs/en/integrations/seeedstudio-recamera.md?plain=1#L62 https://github.com/Seeed-Studio/SSCMA-Micro https://docs.ultralytics.com/modes/predict/#key-features-of-predict-mode https://github.com/Seeed-Studio/reCamera-OS/tree/sg200x-reCamera/external/br2-external